The analysis of cardiac electrophysiological activity such as electrocardiograms (ECGs) increasingly informs the management of cardiac disorders and irregularities, such as atrial fibrillation. Skin surface ECG signal analysis is typically based on waveform time domain parameters, and may depend heavily on the quality of the signal received and analyzed. Thus, the higher quality of the recorded ECG, the more reliable the information that can be extracted from the recorded waveform. Such waveforms may be analyzed (typically after standard filtering and “cleaning” of the signal) for various indicators useful to detect cardiac events or status, such as cardiac arrhythmia detection and characterization. Indicators may include heart rate variability (HRV), cardiac wave morphology, R wave-ST segment and T wave amplitude analysis.
Recently, hand-held monitors have been developed that work with mobile telecommunications devices. For example, U.S. Pat. Nos. 8,509,882 and 8,301,232 to Albert describe hand-held ECG devices that may be used with mobile telecommunications devices. Because the accurate interpretation and analysis of ECG signals requires relies upon the quality of the signals received/recorded, techniques for cleaning up signals to remove artifacts has been proposed. Known cardiac monitoring systems that may include detection and characterization of cardiac markers many not be sufficiently robust to handle signals, particularly including signals taken with handheld ECG devices including those mentioned above, which may use dry electrodes that can be held against the patient's skin by the patient or medical professional, which may result in artifacts such as motion artifacts, environmental artifacts, and contact artifacts. Inaccurate and subjective evaluation and diagnosis may cause unexpected delay in cardiac rhythm management, drug delivery and emergency treatment.
In addition, existing apparatuses and methods for de-noising ECG signals are typically applied before interpreting an ECG signal. As mentioned, although it is beneficial to interpret signals that are as noise-less and representative of the actual electrical activity of the heart as possible, most apparatuses and methods for de-noising alter a putative ECG signal before it is analyzed for cardiac markers, possibly removing non-noise signal when removing artifactual components. Thus, in some variations it may be beneficial to at least partially interpret signals before and/or during de-noising procedures.
An example of an idealized version of a typical ECG waveform is shown in FIG. 9, illustrating multiple cycles. In general, ECG devices may include one, two, three, or more (e.g., six, twelve) leads. It would be helpful to provide robust ECG signal conditioning and analysis of any or all leads including 12-lead ECG and multi-channel intra-cardiac electrograms (ICEG) devices.
Furthermore, known waveform morphology parameter analysis systems, such as P wave, QRS complex, ST segment, T wave analysis systems, are used for cardiac arrhythmia monitoring and identification, e.g., of atrial fibrillation (AF), myocardial ischemia (MI) and ventricular tachycardia/fibrillation (VT/VF). However, known waveform morphology parameter analysis is often subjective and time-consuming, and requires extensive medical expertise and clinical experience for accurate interpretation and proper cardiac rhythm management.
Known clinical methods use electrophysiological (EP) surface ECG and ICEG signal voltage amplitude analysis for arrhythmia detection to identify and characterize cardiac abnormality and arrhythmia related information (such as timing, energy). Known clinical diagnosis standards may be of limited value in some cases. For example, myocardial ischemia and infarction detection is usually based on ST segment voltage deviation for ischemia event detection (e.g. 0.1 mV elevation). Known methods for cardiac arrhythmia analysis, such as myocardial ischemia event detection and evaluation, rely on a repolarization signal portion, such as ST segment and T wave morphology changes. Such methods lack capability for quantitative characterization of cardiac arrhythmia severity and may cause a false alarm. For example amplitude voltage ST segment measurement fails to provide a reliable severity level of an ischemia event. Heart rate variability is unable to provide an arrhythmia urgency level.
The apparatuses, including systems and devices, and methods implementing or operating them described herein may address the deficiencies and related problems discussed above.